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🔔 Subscribe to YouTube ChannelWhat is RAG (Retrieval-Augmented Generation)?
RAG stands for Retrieval-Augmented Generation. In simple terms, RAG allows an Artificial Intelligence (AI) model to answer questions accurately by fetching facts and context directly from your own private documents, manuals, or databases.
Key Components of a RAG System
A standard RAG pipeline is built using the following core building blocks:
- User: The person who initiates the request by asking a specific question.
- Knowledge Sources: The original raw files (PDFs, Word documents, text files, product manuals, or databases).
- Document Loader: Reads and extracts clean text from various file formats.
- Text Splitter: Breaks long documents into bite-sized segments called chunks for accurate searching.
- Embedding Model: Translates text chunks into mathematical vectors that capture their semantic meaning.
- Vector Database: Specialized storage optimized to search and retrieve embeddings based on meaning.
- Retriever: Identifies and extracts the most relevant document chunks matching the user query.
- Prompt Construction: Merges the original user query with the retrieved context into a single structured prompt.
- Large Language Model (LLM): Processes the combined prompt to craft a clear, coherent, and grounded response.
- Final Answer: The refined output delivered directly back to the user.
Real-World Example: HR Assistant
When an employee asks, "How many sick leaves can I take?", the RAG system performs the following flow:
- It searches company HR policy documents.
- The retriever extracts the exact paragraph covering leave allowances.
- The LLM reads that excerpt and generates a precise, reliable response tailored specifically to the company's guidelines.
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